LLMChain的使用_llmchain invoke

import ChatGLM
from langchain.chains import LLMChain

from langchain_core.prompts import ChatPromptTemplate

from langchain.chains import SimpleSequentialChain

prompt = ChatPromptTemplate.from_template("What is the best name to describe a company that makes {product},please respond in chinese, must contian {a2}")
llm = ChatGLM.ChatGLM_LLM()
chain_one = LLMChain(
    llm=llm,
    prompt=prompt,
    verbose=True
)
print(chain_one.invoke({"product":"shoes","a2":"深圳"}))
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from langchain.prompts import PromptTemplate
from langchain_core.prompts import ChatPromptTemplate
import gradio as gr
from langchain.chains import LLMChain
from LLMs import myllm
hf = myllm()
template = """{question}"""
prompt = PromptTemplate.from_template(template)
chain = prompt | hf


prompt = ChatPromptTemplate.from_template("What is the best name to describe a company that makes {product},please respond in chinese, must contian {words}")
prompt2 = ChatPromptTemplate.from_template("{input}")

llm = hf
chain_one = LLMChain(
    llm=llm,
    prompt=prompt,
    verbose=False
)

chain_one2 = LLMChain(
    llm=llm,
    prompt=prompt2,
    verbose=False
)

chains =  {"input": chain_one } |  chain_one2

print(chains.invoke({"product":"shoes","words":"深圳"}))


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@chain的使用

from langchain.prompts import PromptTemplate
from langchain_core.prompts import ChatPromptTemplate
import gradio as gr
from langchain.chains import LLMChain
from langchain_core.runnables import chain
from langchain_core.output_parsers import StrOutputParser
from LLMs import myllm
hf = myllm()
prompt = ChatPromptTemplate.from_template("What is the best name to describe a company that makes {product},please respond in chinese, must contian {words}")
prompt2 = ChatPromptTemplate.from_template("{input}")

llm = hf
chain_one = LLMChain(
    llm=llm,
    prompt=prompt,
    verbose=False
)

chain_one2 = LLMChain(
    llm=llm,
    prompt=prompt2,
    verbose=False
)

@chain
def  myadd(name:str):
    print(name)
    # return "myadd"
    return name

# chains = {"input": chain_one } | prompt2|llm | StrOutputParser()

chains2 =  {"input": chain_one } | chain_one2 | myadd
# print(chains.invoke({"product":"shoes","words":"深圳"}))
print(chains2.invoke({"product":"shoes","words":"北京"}))

# print(chains.predict(product="shoes",words="深圳"))

# print(chains.generate(
#     [
#         {"product":"shoes","words":"深圳"},
#        {"product":"shoes","words":"北京"},
#      ]
#     ))

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